AI Audit & Security Framework

Beyond simple testing, an AI audit is a formal, evidence-based evaluation. It requires scoping the attack surface, executing adversarial campaigns, documenting proof of vulnerabilities, and aligning risks with regulatory frameworks (EU AI Act, ISO 42001). This is Part 0 of the IJCAI 2022 tutorial.

Explore the methodology →

The Four Pillars

A rigorous audit follows a repeatable, standardized process.

Scope: Threat modeling & asset mapping
Test: Red teaming & fairness evaluation
Analyze: Risk scoring & reproducibility
Report: Evidence trails & remediation

Core Concepts

The foundations of systematic, regulatory-aligned AI security testing.

Why Audits Matter

An audit answers the critical question: Can this system withstand malicious intent? It moves beyond QA testing ("does it work?") to adversarial validation ("how can it be broken?"). In a regulated landscape, audits provide the necessary proof of safety.

Compliance: Satisfies EU AI Act, NIST AI RMF, and ISO/IEC 42001 requirements.
Liability: Creates a documented baseline of diligence before production deployment.
Consumer Trust: Ensures algorithmic fairness and protects against disparate impact.

Standardized Frameworks

A professional audit does not rely on ad-hoc hacking. It maps every finding to recognized industry taxonomy, ensuring that vulnerabilities are classified, understood, and actionable by development teams.

MITRE ATLAS: Mapping adversarial tactics, techniques, and procedures (TTPs).
OWASP LLM Top 10: Categorizing critical vulnerabilities (e.g., Prompt Injection, Insecure Output Handling).
CVSS Scoring: Quantifying the severity of AI-specific flaws.

The 4 Layers of an AI Scope

A complete audit examines the entire ecosystem, not just the model weights.

1. The Data & Model Layer

Testing the foundational integrity of the machine learning model.

Training data poisoning vulnerabilities
Model inversion and data extraction
Algorithmic bias and fairness testing

2. The Application Layer

Auditing the interfaces where users and systems interact with the AI.

Direct prompt injection and jailbreaking
Indirect injection via RAG (Retrieval-Augmented Generation)
Denial of Wallet / Resource exhaustion

3. The Agentic & System Layer

Evaluating autonomous behaviors and external integrations.

Excessive agency and unauthorized tool execution
Server-Side Request Forgery (SSRF) via LLM
Memory poisoning in conversational agents

4. The Governance Layer

Reviewing the guardrails, policies, and operational controls.

Audit trail logging and monitoring (Drift detection)
Human-in-the-loop (HITL) enforcement mechanisms
Input sanitization and output filtering

The Audit Workflow

Each phase is methodical and repeatable. No phase is skipped; each produces specific artifacts.

Phase 1: Define Scope & Threat Model

Identify the system's boundaries, sensitive data, and potential adversaries. This establishes the rules of engagement.

Map the architecture (Model, RAG databases, APIs, UI).
Identify applicable regulations (e.g., EU AI Act High-Risk criteria).
Develop an AI-specific threat model (adapted STRIDE or PASTA).

Phase 2: Execution & Red Teaming

Run structured, adversarial campaigns using both automated tooling and manual exploitation.

Fuzzing application inputs to discover edge cases.
Executing multi-turn jailbreaks and indirect prompt injections.
Testing for algorithmic fairness (Demographic Parity, Equal Opportunity).

Phase 3: Evidence & Analysis

A finding is only valid if it can be proven and its risk accurately assessed.

Create reproducible Proofs of Concept (PoCs) for each exploit.
Score vulnerabilities using CVSS and map them to OWASP/MITRE.
Assess the business impact (data leak, reputational damage, financial loss).

Phase 4: Reporting & Remediation

Deliver actionable intelligence to stakeholders, from the board of directors to the engineering teams.

Draft an Executive Summary highlighting critical business risks.
Provide technical remediation playbooks (e.g., input validation, guardrails).
Define acceptance criteria for re-testing and continuous monitoring.

Deep dives into specific audit topics and attack vectors.

LLM Red Teaming

Structured attack campaigns targeting language models and RAG pipelines.

Prompt Injection Attacks

Direct and indirect injection, with advanced test cases and mitigation strategies.

AI Agent Security Audit

Evaluating autonomous tool use, permission scopes, and memory poisoning.

AI Governance & EU AI Act

Aligning technical security audits with regulatory compliance and consumer protection.